GitXplorerGitXplorer
f

fairseq

public
30184 stars
6374 forks
1275 issues

Commits

List of commits on branch main.
Verified
da8fb630880d529ab47e53381c30ddc8ad235216

Change Meta AI to FAIR (#5346)

ccbalioglu committed a year ago
Verified
c7c478b92fe135838a2b9ec8341495c732a92401

fix iterator when loading from checkpoint (#5344)

0000Justin000 committed a year ago
Verified
7409af7f9a7b6ddac4cbfe7cafccc715b3c1b21e

Keep task level checkpoint key name generic (#5330)

ppiyush-kansal committed a year ago
Verified
e29f53bfea67fd9e81c3da374daac4b472ba6bda

initial revision (#5328)

ppiyush-kansal committed a year ago
Verified
b5d89cddc9e4a0af831d2aafc1ba7dbf0f1b10d0

Update align_and_segment.py (#5317)

vvineelpratap committed a year ago
Verified
4db264940f281a6f47558d17387b1455d4abd8d9

Add batchnorm option to hubert/wav2vec2 positional convolution layer for hubert bf16 models (#5285)

ttuanh208 committed a year ago

README

The README file for this repository.



Support Ukraine MIT License Latest Release Build Status Documentation Status CicleCI Status


Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

We provide reference implementations of various sequence modeling papers:

List of implemented papers

What's New:

Previous updates

Features:

We also provide pre-trained models for translation and language modeling with a convenient torch.hub interface:

en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
# 'Hallo Welt'

See the PyTorch Hub tutorials for translation and RoBERTa for more examples.

Requirements and Installation

  • PyTorch version >= 1.10.0
  • Python version >= 3.8
  • For training new models, you'll also need an NVIDIA GPU and NCCL
  • To install fairseq and develop locally:
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./

# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./

# to install the latest stable release (0.10.x)
# pip install fairseq
  • For faster training install NVIDIA's apex library:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
  --global-option="--fast_multihead_attn" ./
  • For large datasets install PyArrow: pip install pyarrow
  • If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run .

Getting Started

The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.

Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands.

We also have more detailed READMEs to reproduce results from specific papers:

Join the fairseq community

License

fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.

Citation

Please cite as:

@inproceedings{ott2019fairseq,
  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},
}